Kastor: Turning Physics Foundation Models Into Efficient Generative PDE Simulators
qberthet · x · 2026-09-04
arXiv 2608.06107 (Google team) proposes Kastor, a comprehensive methodology for adapting a deterministic physics foundation model into an efficient, accurate generative surrogate:
- Two-stage inference: a large-stride causal auto-regressive model plus a non-causal temporal super-resolution network, cutting error accumulation while minimizing compute;
- Mean Prediction Regularization (MPR): a novel objective constraining the model to predict the deterministic distribution mean under null noise, dramatically improving FGN and diffusion-based emulators;
- Spatial gradient matching improves accuracy and physical fidelity as measured by power spectrum density.
The work addresses long-standing error accumulation and stochasticity issues in autoregressive ML emulators. The poster is teaching an "AI for scientific computing" course around this topic.
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